计算机应用 ›› 2017, Vol. 37 ›› Issue (11): 3261-3269.DOI: 10.11772/j.issn.1001-9081.2017.11.3261

• 网络与通信 • 上一篇    下一篇

基于压缩感知的无线传感器网络数据收集研究综述

乔建华1,2, 张雪英1   

  1. 1. 太原理工大学 信息工程学院, 太原 030024;
    2. 太原科技大学 电子信息工程学院, 太原 030024
  • 收稿日期:2017-05-19 修回日期:2017-07-27 出版日期:2017-11-10 发布日期:2017-11-11
  • 通讯作者: 张雪英
  • 作者简介:乔建华(1975-),女,山西吕梁人,副教授,博士研究生,主要研究方向:无线传感器网络、压缩感知;张雪英(1964-),女,河北行唐人,教授,博士,主要研究方向:语音信号处理、多媒体通信、物联网。
  • 基金资助:
    山西省自然科学基金资助项目(2013011019-1)。

Compressed sensing based data gathering in wireless sensor networks: a survey

QIAO Jianhua1,2, ZHANG Xueying1   

  1. 1. School of Information Engineering, Taiyuan University of Technology, Taiyuan Shanxi 030024, China;
    2. School of Electronic and Information Engineering, Taiyuan University of Science and Technology, Taiyuan Shanxi 030024, China
  • Received:2017-05-19 Revised:2017-07-27 Online:2017-11-10 Published:2017-11-11
  • Supported by:
    This work is partially supported by the Natural Science Foundation of Shanxi Province (2013011019-1).

摘要: 为了对无线传感器网络的压缩数据收集有一个全面的认识和评估,对到目前为止国内外的相关研究成果作了一个系统的介绍。首先,介绍了压缩数据收集及改进方法的框架的建立;然后,分别根据无线传感器网络的传输模式和压缩感知理论的三要素,对压缩数据收集方法分类进行了阐述;接下来,说明了压缩数据收集的自适应和优化问题,与其他方法的联合应用,及实际应用范例;最后,指出了压缩数据收集存在的问题和未来的发展方向。

关键词: 无线传感器网络, 压缩感知, 数据收集, 路由, 稀疏投影

Abstract: In order to have a comprehensive understanding and evaluation for the Compressive Data Gathering (CDG) in Wireless Sensor Network (WSN), a systematic introduction to the related research results at home and abroad so far was made. Firstly, the establishment of the frameworks of CDG and improved methods was introduced. Secondly, according to the transmission modes of WSN and Compressed Sensing (CS) theory respectively, the various methods of CDG were elaborated by classification. Then the problems of adaptation and optimization of CDG, the application of CS combined with other methods, and some examples of practical application were illustrated. Finally, the disadvantages in CDG and the development directions of CDG were pointed out.

Key words: Wireless Sensor Network (WSN), Compressed Sensing (CS), data collection, routing, sparse projection

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